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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.
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.
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.
A love for data analysis and statistics, research aptitude and motivation to do great research work.
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30 day money back guarantee.
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Desktop, iOS and Android.
Certificate of completion.
|Section 1: Introduction|
This lecture tells you about instructor expertise and his SPSS bio and how he can make a significant difference to your research life.
|Section 2: Downloading and Installing SPSS|
This video lecture guides students in a step wise manner about downloading IBM SPSS Statistics 24 and installing it on a Windows laptop/computer.
Downloading SPSS Grad Pack: Student Version
|Section 3: Conceptual Foundation of Statistics|
Statistics: Definition and Types
Parametric vs Non-Parametric Statistics: Assumptions
|Section 4: Data Entry: Learning to Enter Data in SPSS|
Conceptualizing Variables: IV, DV, Control, Moderators & Mediating VariablesPreview
Variable Type Numeric: Defining Names, Width, Decimal & Labels for variables
Variable Type: Comma & Dot
Variable Type: Scientific Notation
Variable Type: Date and Time Stamps
Variable Type: Dollar
Variable Type: Custom Currency
Variable Type: String
Variable Type: Restricted Numeric
Defining Values & Labels
Defining Missing Values: Discrete, Range & System-Missing Values
Setting Columns & Alignment
Defining Measures: Scales of Measurement
|Section 5: Working with Various File Types in SPSS|
This lecture gives an overview of various types of data files that can be opened in SPSS Statistics.
In this lecture you will learn how to open an Excel data file in SPSS.
In this lecture you will lean how to open a CSV file type in SPSS using data import wizard.
|Section 6: Independent Sample t-test: Comparing Two Independent Group Means|
Independent sample t-test: Defining input optionsPreview
Independent sample t-test: Interpreting descriptive output (Mean, SD, SE)
Independent Sample t-test: Interpreting Levene's test, t, p, SE & 95% CI
APA Style write-up for Independent Sample t-test
|Section 7: Paired Sample t-test: Comparing Differences between Two Correlated Group Means|
When to use Paired Sample t-test?Preview
Calculating Paired Sample t-test in SPSS
Interpreting Paired Sample t-test Output
APA Style write-up for Paired Sample t-test
|Section 8: One-Way ANOVA: Comparing Differences between More than Two Groups|
When to Use One-Way ANOVA?Preview
Calculating One-Way ANOVA in SPSS
Interpreting ANOVA output: Descriptive Statistics
Interpreting Output: ANOVA Summary Table
Doing Post-hoc analysis in ANOVA: Homogeneity of Variance Test & Post-hoc
Trend Analysis & Means Plot in ANOVA
Contrast Analysis in ANOVA
|Section 9: Linear Regression: Cause and Effect Analysis of One IV on One DV|
What is regression?Preview
When to Use Linear Regression Vs. Multiple Regression?
Defining SPSS Input Options for Linear Regression
Interpreting Linear Regression Output: Variables & Model Summary
Interpreting Linear Regression Output: Constant, B, Beta, SE & t
|Section 10: Multiple Regression: Causal Effect of Many IVs on One DV|
What is Multiple Regression?Preview
Assumptions of Multiple Regression: Linearity & Testing Linearity in SPSS
Assumptions 2: Independence of Errors/Lack of Autocorrelations & Testing in SPSS
Assumptions 3: Homoscedasticity of Errors & Testing it in SPSS
Assumptions 4: Multivariate Normality & Testing it in SPSS
Assumptions 5: Multicollinearity & Testing it in SPSS
Choosing a Method of Multiple Regression: Enter Method
Choosing a Method of Multiple Regression: Stepwise and Forward Selection Method
Choosing a Method of Multiple Regression: Backward Elimination Method
Running Stepwise and Forward Selection Method of Regression in SPSS
Choosing a Method of Multiple Regression: Remove Method
|Section 11: Hierarchical Regression Analysis|
What is Hierarchical Regression Analysis and when to use it?Preview
Setting Data and Defining Model in Hierarchical Regression
Refining Model and Detecting Multicollinearity through Correlation Matrix
Taming Bad Data: Using beta, R squared and p values to further refine model
Interpreting the Output of Hierarchical Regression
|Section 12: Exploratory Factor Analysis|
What is Factor Analysis?Preview
Understanding Latent Variables and Indicators in FA
Sample Researches Using FA in Social Science & Engineering
Historical Origin of FA & Its Application in Test Construction
Exploratory Factor Analysis vs. Confirmatory Factor Analysis (EFA vs. CFA)
Setting Data for Factor Analysis
Understanding "Selection Variable"
Univariate Descriptives & Initial Solutions: Descriptive
Correlation Matrix: Coefficients, Significance, Determinant, KMO & Bartlett's
Understanding Inverse, Reproduced, Anti-Image
Extraction Method: Principle Componenet Analysis
Extraction Method: Principle Axis Factoring
Extraction Method: Maximum Likelihood Estimation
Choosing Correlation vs. Covariance Matrix for Factor Analysis
Interpreting Correlation Matrix & Unrotated Factor Solution
Determining number of factors: Scree Plot vs. Kaiser's eigen value criteria
Factor Rotation: What it is and why its done?
Rotation Methods: Varimax, Quartimax, Equamax, Direct Oblimin, Promax
Calculating Factor Scores: Regression, Bartlett, Anderson-Rubin
Factor Score Coefficient Matrix
Missing Value Analysis: Listwise, Pairwise, Replace with Mean
Sort by Size & Suppressing Smaller Coefficients
Project in Factor Analysis Part 1: Identifying Dimensions of Personality
Project in Factor Analysis Part 2: Identifying Dimensions of Personality
Project in Factor Analysis Part 3: Identifying Dimensions of Personality
Project in Factor Analysis Part 4: Factor Naming
Project in Factor Analysis Part 5: Reliability Analysis of Factors
Project in Factor Analysis Part 6: Presenting Results in APA Style
Dr. Sanjay Singh has over 7 years of teaching and research experience and has done his Masters and Doctoral Degree from University of Delhi, India. He have been recipient of Erasmus Mundus-WILLPower fellowship awarded by European Union. During the tenure of his fellowship he studied and worked at the University of Padova, Venice, Italy at the Centre for Risk and Decision-making (CeRD).
He has worked for prestigious institutions like University of Delhi & Indian Institute of Technology, Delhi, and is currently a full time faculty at a premier business school located in Delhi. He has been involved in academic teaching and training for over 7 years along with training and consulting to different companies for data analysis, psychometric assessment, and development of research and analytical skill that can enhance the productivity and growth of people and organizations.
Dr. Singh has trained at reputed organizations like Ernst & Young, DRDO, University of Delhi for IBM SPSS/AMOS and has received excellent rating for his training programs. He has consulted analytics, psychometric and human resource organizations in India and abroad for quantitative project planning, developing customized and culture fair psychometric tests and refinement of quantitative models.He has also consulted students and faculty from reputed institutions like London School of Economics, UK, University of Tallin, Estonia, University of Sydney, Australia, and Faculty of Management Studies, University of Delhi on research and analytics related projects.
Dr. Singh strongly believes that learning can be fun and use of technology in learning can make it even more exciting. As a researcher he loves working at the interface where scientific research, human behaviour and technology meet.
I am Yogita Aggarwal with teaching and research experience of over 10 years. Teaching and sharing my knowledge with others have been my passion. I am excited to convey my ideas and expertise to learners around the world through Udemy.I hope you will greatly benefit with content of courses that I have designed and be successful in your goals.