SPSS Masterclass: Learn SPSS From Scratch to Advanced
Loại khoá học: Business Analytics & Intelligence
The Only Course You Will Ever Need to Learn IBM SPSS Statistics, Research, & Data Analysis Scratch to Advanced Level
Mô tả
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.
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33 sections
How to get answer to your queries fast?
00:44
Downloading and Installing IBM SPSS Statistics 24 on Windows
09:58
Downloading SPSS Grad Pack: Student Version
01:23
Practice dataset, PPT and Resources
00:14
Chapter 1 Introduction: Conceptual Foundations
00:01
Chapter 2 Importing Data in SPSS
00:01
Chapter 3 Data Entry in SPSS
00:01
Chapter 4 Data Manipulation in SPSS
00:01
Chapter 5 Descriptive Statistics in SPSS
00:01
Chapter 6 t-Test - Independent Sample and Correlated
00:01
Chapter 7 One Way ANOVA
00:01
Chapter 8 Linear Regression
00:01
Chapter 9 Multiple Regression
00:01
Chapter 10 Hierarchical Regression Analysis
00:01
Chapter 11 Exploratory Factor Analysis
00:01
Great References on Quantitative Methods
01:58
Philosophy of Research
00:06
Research Papers with Fascinating Ideas
00:16
Statistics: Definition and Types
03:09
Parametric vs Non-Parametric Statistics: Assumptions
09:50
Statistics Basics
2 questions
Conceptualizing Variables: IV, DV, Control, Moderators & Mediating Variables
07:09
Variable Type Numeric: Defining Names, Width, Decimal & Labels for variables
06:01
Variable Type: Comma & Dot
05:24
Variable Type: Scientific Notation
02:21
Variable Type: Date and Time Stamps
02:55
Variable Type: Dollar
01:14
Variable Type: Custom Currency
04:24
Variable Type: String
01:51
Variable Type: Restricted Numeric
03:16
Defining Values & Labels
03:45
Defining Missing Values: Discrete, Range & System-Missing Values
04:45
Setting Columns & Alignment
02:45
Defining Measures: Scales of Measurement
11:02
Types of Data Files in SPSS Statistics
11:31
Opening an Excel data file in SPSS
05:33
Opening a Comma Separated or CSV file type in SPSS
07:39
Dataset and Resources: RECODE Function
00:06
COMPUTE VARIABLE function: What it is and What it can do for us?
01:53
Calculating Total using COMPUTE function
08:37
Exercise: Try COMPUTE using IF
01:29
Exercise Solution: COMPUTE using IF
02:21
RECODE FUNCTION: Why to Recode Variable?
06:33
Why We have Two RECODE Functions?
01:45
How to do RECODE INTO DIFFERENT VARIABLE in SPSS?
10:40
COMPUTING Total After RECODE
01:59
Recode into Same Variable
08:50
Setting Data for Descriptive Analysis
03:15
Types of Descriptive Statistics
06:22
Understanding Three Different Descriptive Tabs in SPSS
05:13
Calculating Frequencies
10:00
Descriptives Analysis Using Crosstab
04:28
Measures of Central Tendency: Mean, Median, Mode - Concept and Uses
04:09
Calculating and Interpreting Mean, Median & Mode
05:33
Confirming Mode with Frequencies
02:10
Explore Option: Calculating Grouped Descriptives
05:16
Explore Option: Interpreting Groupwise Mean and 95% Confidence Interval of Mean
02:48
5% Trimmed Mean: Concept, Use & Interpretation
01:01
Explore: Median, Standard Deviation, Variance, Minimum, Maximum, & Range
01:55
Quartiles and Inter-Quartile Range using Explore Option
02:44
Skewness and Kurtosis: Fundamentals Explained
02:39
Calculating & Interpreting Significance Level of Skewness
05:19
Kurtosis: Calculation, Interpretation and Understanding Significance Level
03:13
Standard Error of Mean: Concept, Calculation & Interpretation
01:38
Independent sample t-test: Defining input options
07:25
Independent sample t-test: Interpreting descriptive output (Mean, SD, SE)
03:57
Independent Sample t-test: Interpreting Levene's test, t, p, SE & 95% CI
04:13
APA Style write-up for Independent Sample t-test
03:47
When to use Paired Sample t-test?
02:55
Calculating Paired Sample t-test in SPSS
04:37
Interpreting Paired Sample t-test Output
03:02
APA Style write-up for Paired Sample t-test
02:18
When to Use One-Way ANOVA?
01:55
Calculating One-Way ANOVA in SPSS
03:08
Interpreting ANOVA output: Descriptive Statistics
03:05
Interpreting Output: ANOVA Summary Table
03:51
Doing Post-hoc analysis in ANOVA: Homogeneity of Variance Test & Post-hoc
05:47
Trend Analysis & Means Plot in ANOVA
05:55
Contrast Analysis in ANOVA
03:20
What is regression?
01:11
When to Use Linear Regression Vs. Multiple Regression?
01:44
Defining SPSS Input Options for Linear Regression
02:03
Interpreting Linear Regression Output: Variables & Model Summary
05:06
Interpreting Linear Regression Output: Constant, B, Beta, SE & t
05:31
What is Multiple Regression?
07:38
Assumptions of Multiple Regression: Linearity & Testing Linearity in SPSS
04:04
Assumptions 2: Independence of Errors/Lack of Autocorrelations & Testing in SPSS
02:28
Assumptions 3: Homoscedasticity of Errors & Testing it in SPSS
01:44
Assumptions 4: Multivariate Normality & Testing it in SPSS
00:49
Assumptions 5: Multicollinearity & Testing it in SPSS
03:06
Choosing a Method of Multiple Regression: Enter Method
03:24
Choosing a Method of Multiple Regression: Stepwise and Forward Selection Method
05:32
Choosing a Method of Multiple Regression: Backward Elimination Method
04:30
Running Stepwise and Forward Selection Method of Regression in SPSS
06:07
Choosing a Method of Multiple Regression: Remove Method
03:59
What is Hierarchical Regression Analysis and when to use it?
01:55
Setting Data and Defining Model in Hierarchical Regression
04:57
Refining Model and Detecting Multicollinearity through Correlation Matrix
09:50
Taming Bad Data: Using beta, R squared and p values to further refine model
03:36
Interpreting the Output of Hierarchical Regression
14:27
Personality Dataset
00:03
What is Factor Analysis?
02:23
Understanding Latent Variables and Indicators in FA
01:11
Sample Researches Using FA in Social Science & Engineering
06:03
Historical Origin of FA & Its Application in Test Construction
04:43
Exploratory Factor Analysis vs. Confirmatory Factor Analysis (EFA vs. CFA)
05:27
Setting Data for Factor Analysis
02:41
Understanding "Selection Variable"
02:57
Univariate Descriptives & Initial Solutions: Descriptive
01:28
Correlation Matrix: Coefficients, Significance, Determinant, KMO & Bartlett's
04:40
Understanding Inverse, Reproduced, Anti-Image
04:07
Extraction Method: Principle Component Analysis
03:03
Extraction Method: Principle Axis Factoring
01:43
Extraction Method: Maximum Likelihood Estimation
00:53
Choosing Correlation vs. Covariance Matrix for Factor Analysis
06:02
Interpreting Correlation Matrix & Unrotated Factor Solution
07:37
Determining number of factors: Scree Plot vs. Kaiser's eigen value criteria
08:22
Factor Rotation: What it is and why its done?
06:35
Rotation Methods: Varimax, Quartimax, Equamax, Direct Oblimin, Promax
08:11
Calculating Factor Scores: Regression, Bartlett, Anderson-Rubin
03:53
Factor Score Coefficient Matrix
01:46
Missing Value Analysis: Listwise, Pairwise, Replace with Mean
02:54
Sort by Size & Suppressing Smaller Coefficients
06:21
Project in Factor Analysis Part 1: Identifying Dimensions of Personality
14:29
Project in Factor Analysis Part 2: Identifying Dimensions of Personality
15:38
Project in Factor Analysis Part 3: Identifying Dimensions of Personality
05:39
Project in Factor Analysis Part 4: Factor Naming
13:35
Project in Factor Analysis Part 5: Reliability Analysis of Factors
22:37
Project in Factor Analysis Part 6: Presenting Results in APA Style
08:58
Chi Square Test: Introduction and When to Use Chi-Square Test?
03:50
Assumptions of Chi-square Test
03:54
Formula for Calculation of Chi-Square Test
01:14
Setting Data for Calculation of Chi-Square using Crosstabs Option
04:39
Testing Assumptions of Chi-Square test Using Crosstabs Option
03:26
Interpreting Output of Chi-Square Test and APA Style Reporting
02:31
One-way Chi Square: When to use and how its different from two-way Chi square?
07:11
Setting Data for One-way Chi Square Test
02:08
Weigh Cases, Calculation, Interpretation & APA Write-up for One-Way Chi Square
07:26
Practice Data set for One-Way Chi square
03:08
Introduction to Reliability Analysis
02:03
What is Reliability?
03:26
Reflective vs. Formative Models of Scale
05:28
Should We Report Cronbach's Alpha or Composite Reliability?
01:46
Type of Reliability: Test-Retest Reliability
02:02
Type of Reliability: Parallel Form
01:58
Type of Reliability: Internal Consistency Reliability
03:33
Understanding Cronbach's Alpha
01:47
Assumptions of Cronbach's Alpha
08:59
Formula of Cronbach's Alpha
02:27
Range of Cronbach's Alpha
04:41
Calculating Reliability: Understanding Scale if an Item is Deleted Option
02:38
Interpreting Case Processing Summary & Alpha Coefficient
00:55
Improving Reliability of a Scale: Diagnosing Missing Values
06:17
Improving Reliability: Diagnosing Scale Mean and Variances
02:40
Improving Reliability: Diagnosing Item-Total Correlations
04:52
Improving Reliability: Removing Ambiguous and Redundant Items
07:59
Item Discrimination Index
03:57
Graphs & Data Visualization in SPSS: An Introduction
02:03
Which Graph is Suitable for Me: Rules for Creating Graphs Part 1
11:50
Which Graph is Suitable for Me: Rules for Creating Graphs Part 2
07:23
Creating a Bar Diagram in SPSS
05:38
How to Change Background Color of Bar Diagram in SPSS?
01:35
How to Change Color & Patterns of Bars in Bar Diagram?
02:52
How to Rename X & Y Axes of Bar Diagram in SPSS?
02:02
Understanding Error Bars over Bar Diagram: What, Why & How?
04:31
How to Create Bar Diagrams with Error Bars in SPSS?
03:36
How to Use Multipliers (Standard Error & Standard Deviation) in Bar Diagrams ?
09:08
Creating Clustered Bar Diagrams in SPSS
08:46
Pie Charts: Understanding and Setting Dataset
03:20
Pie Charts Vs. Bar Diagram: When to Use Pie Vs. Bar Diagram?
04:45
Pie Chart: How to Change the Color of Pie?
01:32
Pie Chart: How to Merge Slices?
03:04
1. What is Logistic Regression?
00:36
Logistic Regression (External Resource)
00:07
2. Understanding the Logistic Regression Model
05:40
3. Understanding and Logistic Regression Model: Shape, Logit and Probabilities
07:58
4. Understanding the Equation of Logistic Regression
03:41
5. Requirements for Logistic Regression Analysis
00:45
6. Assumptions of Logistic Regression
05:35
7. Concept of Odd Ratios (in Brief)
01:24
8. Setting Data and Understanding the Data File
04:23
9. How to Code the Binary Dependent Variable in Logistic Regression
04:28
10. Understanding Block Option and Interaction Option
03:14
11. Selecting "Method" and Coding Categorical Variable as "Dummy" Variable
06:35
12. Understanding Save Option: Predicted Probabilities & Group Membership
03:12
13. Understanding Save Option: Influence - Cook's Distance & DFBeta Options
03:44
14. Understanding Save: Residuals – Standardized
01:50
15. Understanding Classification Plots Option
01:29
16. Understanding Hosmer-Lemeshow Goodness of Fit Test Option
01:51
17. Understanding Case-wise Listing of Residuals
01:26
Understanding Correlation of Estimates Option
00:40
Understanding "Iteration History" Option
00:42
Understanding "CI for Exp(B)" Option
01:43
Including Constant in Model
01:08
Understanding "Classification Cutoff .5 & Bootstrapping"
04:49
Output: Understanding Case Processing Summary & Dummy Variable Coding
03:36
Output: Understanding Block 0 vs Other Blocks & Iteration History
02:16
Output: Understanding -2 Log Likelihood & R squares (Cox n Snell, Negelkerke)
06:17
Output: Understanding Classification Table (Sensitivity & Specificity)
04:24
Output: Variables in Equation - Baseline Model Interpretation
01:20
Output: Hosmer-Lemeshow & Contingency Table for Baseline Model
01:13
Output: Interpretation of Hosmer-Lemeshow Test for Default Model
01:36
Output: Interpreting Variables in Equation for Default Model
02:42
Output: Interpreting Wald's Test for Default Model
05:30
Odd Ratios (in Depth): Part 1 - Fundamentals, Derivation & Calculation
18:57
Odd Ratios (in Depth): Part 2 - Calculating Odds of Lung Cancer w/ Smoking
08:27
Interpreting Odd Ratios in Variables in Equation Table
03:10
Interpreting Correlation Table and Understanding Multi-collinearity
03:12
Classification Plot: Interpretation & Application
07:47
Interpreting Case-wise Listing of Residuals Output
03:12
Interpreting Predicted Probabilities and Group Membership
03:54
Interpreting Cook's Distance and DFBeta
04:20
Interpreting Omnibus Test Output
04:06
Explaining Pseudo R Squares: - 2Log Likelihood, Cox & Snell and Negelkerke
06:07
Writing Final equation of Logistic Regression Manually
02:42
APA Style Presentation of Table and Results
17:36
Introduction to Mediation and Moderation Analysis
04:15
Data, PPT & Resources
00:04
Understanding Moderation analysis and its Regression Model - I
04:05
Understanding Moderation analysis and its Regression Model - II
10:51
Statistical Equation of Moderation
05:52
Understanding Mediation: Direct, Indirect and Total Effects
04:32
Understanding Difference Between Moderation & Mediation
09:48
Downloading & Installing Process Macro
09:52
Examples of moderation: Story of Infosys and Uber
07:33
Whats is Mediation: Understanding a Mediation Model
03:33
Whats is Full n Partial Mediation?
02:21
Understanding Direct Indirect & Total Effects
04:41
What is Sobel Test?
01:28
Partially Standardized vs Completely Standardized Indirect Effects
03:39
Understanding Ratios of Indirect effect: Indirect to Total vs Indirect to Direct
02:26
What is Proportion of Variance Explained by Indirect Effect?
02:06
Moderation analysis: Dataset & Hypothesis Development
09:47
Understanding Model Numbers
05:17
Moderation: Variables, Bootstrapping, Covariates, Proposed Moderator W,Z, V, Q
02:19
Moderation> Options: Mean Center for Products
01:37
Moderation>Options: Heteroscedasticity Consistent SE, OLS/ML CI, Data Plotting
01:32
Moderation> Conditioning: Johnson-Neyman
01:49
Moderation: Multi-categorical
00:49
Dealing with Long Names
01:05
Explanation of Output of Moderation Analysis
17:48
Plotting Moderation effect in SPSS and Excel
07:57
APA Style Presentation of Moderation Effect, Chart and Table
02:46
Conceptual Model of Mediation: Does Glucose Mediates the Influence of Diabetes?
03:16
Checking Suitability of Data for Mediation Analysis
03:29
Mediation: M-Variables, Model Number, Bootstrap Sample, and Covariates
04:07
Mediation>Options: OLS/ML Confidence Interval & Effect Size
01:58
Mediation>Option: Sobel Test
01:52
Mediation>Options: Total Effect Model, Compare Indirect Effect, Print Model Cov
01:49
Mediation: Conditioning, Multi-categorical, and Long Names
02:22
Mediation> Output: Understanding Covariance Matrix Output
01:48
Explaining Mediation Output-Part 1
03:33
Explaining Mediation Output-Part 2
04:31
Explaining Mediation Output-Part 3
03:04
Mediation Output: Partially and Fully Standardized Indirect Effects
05:04
Mediation Output: Ratio of Indirect to Total Effect & Indirect to Direct Effect
01:18
Mediation Output: R-squared Mediation Effect Size
00:31
Mediation Output: Normal Theory Test for Indirect Effect
00:43
Mediation Output: Kappa Squared and Why It is Suppressed?
01:31
Calculating Preacher and Kelly's Kappa Squared Manually
09:51
APA Style Presentation of the Results of Mediation Analysis
02:37
Relevant Literature: Mediation and Moderation Analysis using PROCESS
00:13
Dataset and Resources: GLM
00:06
Introduction to (General Linear Models) GLM
00:27
What are General Linear Models (GLM)?
04:57
What are Generalized Linear Models (GLIM)?
05:10
What are Exponential Distributions?
04:05
Examples and Applications of Generalized Linear Models (GLIM)
01:02
General Linear Models (GLM) vs Generalized Linear Models(GLIM)
03:48
Dataset and Resources: One-Way Repeated Measure ANOVA
00:06
What is Repeated Measure Design (Example 1: Depression Study)
04:00
What is Repeated Measure Design (Example 2: Performance under Noise Study)
01:41
What is Repeated Measure Design (Example 3: Control Group Study)
01:44
Should I do Repeated Measure ANOVA or Paired Sample t-test?
00:35
Assumptions of Repeated Measure ANOVA
05:20
Explaining Multivariate Tests
02:31
Understanding Pillai's Trace & Wilk's Lambda
04:54
Understanding Hotelling's Trace
00:58
Understanding Roy's Largest Root
01:40
What is Sphericity: Understanding Sphericity through an Example
04:32
Understanding Mauchly's Test of Sphericity
07:30
Understanding the Dataset
01:39
Formulating Research Question and Hypothesis based on Data
01:52
Understanding "Within-subject Factor Naming"
02:51
Understanding "Measurement Name" Option
02:57
Understanding "Between Subject Factor and Covariate" Options
03:51
Understanding Preliminary Output
00:48
Model: Full Factorial, Build/Custom Terms & Main and Interaction Effects
06:10
Explaining TYPE I, Type II, Type III, and Type IV Sum of Squares
12:24
Contrast: Simple, Polynomial, Repeated, Deviation, Difference, Helmert
17:34
Defining Plots: Exploring All Options
16:57
Introduction to Post-hoc Tests: Two Families of Tests
08:05
When to Use Tukey's and Scheffe's Tests?
02:37
Explaining Bonferroni correction
05:40
Explaining LSD Test
02:24
Tukey,s HSD, Tukey's WSD and SNK Test
12:34
Waller-Duncan, Dunnett’s T, Scheffe, Sidak, Duncan, and Hochberg Gabriel’s Test
13:01
Games Howell, Tamhane's T2 and T3 Tests: Non-parametric Post-hoc Tests
07:42
Introduction to Correlation
01:47
What is Correlation?
01:20
Types of Correlations: Positive and Negative Correlations
03:44
Understanding Correlation coefficient and its Range
03:28
Which Correlation Coefficient to Use and When?
08:58
Introduction to Pearson's Correlation: Origin, Use & Why its so Popular?
03:12
Why it is Called Product Moment Correlation Coefficient?
02:35
Assumptions of Pearson's Product Moment Correlation
06:40
Calculation of r : Deviation Score formula
01:34
Calculation of r: Z-Score Formula
01:26
Calculation of r : Raw Score Formula
02:08
Calculation of r : Co-variance Formula
02:45
Manual Calculation of r using Raw Score Method
02:35
Importance of Correlation Coefficient
02:15
Spurious Correlations: Correlation does not signify causation
03:17
Pearson Correlation as a Coefficient of Variability (R-squared)
02:56
Calculation of r in SPSS: Checking Assumptions
11:28
Calculation of r in SPSS : Understanding Pearson, Two tailed, and Bootstrapping
04:44
Interpretation of Output of r
00:53
Bootstrapping the Correlation Coefficient (r)
01:27
Writing Output of r in APA style
09:51
Fixing the Bootstrap Bug in SPSS 25
10:58
Introduction to Biserial and Point Biserial Correlations
02:22
When to Use Biserial and When to Use Point Biserial Corrleation?
03:49
Calculation and Interpretation of Biserial Correlation in SPSS
04:44
APA Style Reporting of Biserial Correlation Output
00:51
Exercise: Calculating a Point Biserial Correlation between Gender and Salary
01:00
How to Calculate Point Biserial Corrleation in SPSS
01:48
How to Interpret Point Biserial Corrleation in SPSS
03:03
How to Report Point Biserial Correlation Output in APA style
02:47
Introduction to Spearman's Rank Order Correlation Coefficient (Rho)
00:32
When to Use Rank Order Correlation Coefficient: Four Examples
02:56
Who gave Rho and How it is Denoted?
00:16
Assumptions of Spearman's Rank Order Correlation Coefficient
06:50
Understanding the formula Rho and Ranking Method
03:10
How to deal with Tied Ranks while Calculating Rho?
03:57
Should I Rank My Variables First then calculate Rho?
00:30
Calculating and Interpreting Rho in SPSS
02:04
Rho is r on Ranked Data: Proof
03:06
APA style Reporting of Spearman's Rank Order Correlation Coefficient
01:50
Introduction: What is Difference between Association and Correlation
05:48
Understanding Concordant and Discordant Pairs
10:31
Understanding Pairs by Column and Rows Calculation
04:45
Introduction to Kendall's Tau
00:43
When to Use Kendall's Tau instead of Spearman's Rho
03:26
Assumptions of Kendall's Tau
05:24
Range and Interpretation of Kendall's Tau
01:09
Types of Kendall's Tau Coefficients: Tau a, Tau b, Tau c & Kendall's W
01:02
Kendall's Tau a : Concept, When to use and Formula
02:42
Kendall's Tau b and Tau c : Introduction and When to Use Them
03:01
Kendall's Tau b Formula
00:59
Kendall's Tau c : Formula and When to Use
01:42
Kendall's Tau a, Tau b, Tau c, and Kendall's W: A Comparison of Usage
02:29
Kendall's Tau b in SPSS: Checking Tied Ranks
01:22
Kendall's Tau b: APA Style Reporting
01:58
Kendall's Tau c : Assumption Checking
03:38
Kendall's Tau c : Calculation and Interpretation in SPSS
04:01
Kendall's Tau c : APA Style Reporting
01:06
Kendall's W : Introduction and When to Use
04:55
Kendall's W : Understanding the Formula
01:17
Kendall's W in SPSS (using Non-Parametric Auto-Dialogue Box)
05:50
Kendall's W in SPSS (using Non-Parametric Legacy Dialogue Box)
04:55
Kendall's W : APA Style Output Reporting
03:12
Fixing the Bootstrap Bug in SPSS 25
10:58
Descriptive Analysis in SPSS
1 question
Answers to Assignment 1
00:03
Assignment 1: Explanation of Que 1, 2 and 3
20:23
Assignment 1: Explanation of Que 4, 5, 6, 7 and 8
20:44
Assignment 1: Explanation of Que 9 and 10
13:44
Introduction: What is ANCOVA and When to Use It?
04:24
What is meaning of covariate? Does it mean control?
02:19
Ways of ANCOVA and Requirements for Doing ANCOVA
04:19
Understanding Assumptions and Dataset
09:27
Study Design and Dataset
04:26
Understanding Options: DV, Fixed Factors and Random Factors
10:48
Understanding Covariates and WLS weight and why Post-hoc Button Gets Inactive
03:14
Testing Assumptions
20:39
Importance of Controlling Covariate
05:11
Explaining Options: Model Sum of Squares and Contrast
06:49
Explaining Options: Plots
05:04
Explaining Options: Estimated Marginal (EM) Means
05:12
Explaining Options: Compare Main Effects (LSD, Bonferroni, Sidak) & SAVE
03:23
Options: Descriptives, Effect Size Parameter Estimates Homogeneity Test Residual
08:38
Explaining Output: Part 1
09:40
Explaining Output: Part 2
09:40
APA Style Presentation of ANCOVA Results
06:48
What is MANOVA?
01:44
When to Use MANOVA?
04:21
Multivariate Test Decision Tree: When to Use ANOVA, MANOVA, ANCOVA, MANCOVA?
06:33
Assumptions of MANOVA
02:18
Research Questions and Study Design
09:07
Hypotheses Development
03:38
Understanding MANOVA Window in SPSS
05:08
Specifying Model: Full Factorial, Build Terms, and Custom Terms
04:14
Understanding Model Sum of Squares
05:56
What is Contrast?
02:18
Understanding Simple Contrast
02:08
Understanding Repeated Contrast
00:52
Understanding Polynomial Contrast
01:23
Understanding Deviation Contrast
01:40
Understanding Difference and Helmert Contrast
01:45
Understanding Estimated Marginal Means
03:24
Understanding SAVE Option
01:33
Understanding Descriptives, Effect Size, Observed Power, Noncent Parameter
06:43
Understanding Parameter Estimates
00:45
Understanding SSCP Matrix and Residual SSCP Matrix
11:39
Understanding Transformation Matrix and Homogeneity Test
04:15
Understanding Spread and Residual Plots
01:16
Understanding Lack of Fit Test
01:13
Understanding General Estimable Function
00:35
Why Social Scientists Should Learn Programming?
03:35
Programmability Options in SPSS: Python, R and Visual Basic
04:10
Installing and Running Python
05:26
Accessing Python from SPSS
03:55
Understanding Extension Bundle Option
03:26
Three Rules of Writing Python Programme
07:08
Your First Python Programme in SPSS: Hello World
11:17
Unit: Basic Concepts - Understanding Variables and Operators
07:15
Data Types in Python
01:10
What is Data?
01:53
What are data Types?
05:09
What are Data Structures?
04:09
Data Types vs. Data Structures in Python
07:11
Primitive Data Types in Python
03:45
Non-Primitive Data Types: Lists, Stack, Queue, Map
07:37
Non-Primitive Data Types: Tupple, Set, Frozen Set, Dictionary
11:46
Arithmetical Options in Python: Using Python as a Calculator
14:45
Unit- Print Function: Impressing Guide -Printing a Name Million Times in Seconds
14:38
Learning to Print a Paragraph in Python
05:30
Printing a Text Pattern in Python
05:43
Clearing Screen: How to Define Clear Screen Function in Pycharm
10:55
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