Basic SPSS

Basic Statistics and Quantitative methods using SPSS is vital to develop analytical skills. Better decisions are outcome of such skills and it‘s for both researchers and business managers.

What you will learn:
1. Quantitative data collection,
2. Data entry, data cleaning, analysis and decision making using SPSS
3. From problem statement to research questions and frameworks (models) and/or hypothesis testing using SPSS
4. Differentiate and use different statistical tests to solve problems using SPSS
5. Writing Interpretation and report.

The focus of this class is on using SPSS to solve problems.

Subjects that we cover:
- Basic Statistics
- Inferential Statistics
- Type of variables and measurement levels
- Sample and Population
- Data with SPSS
- Entering and Cleaning Data
Descriptive Statistics
- Frequency tables, Pie chart, Bar chart, Histogram
- Mean, Mode, Median
- Skewness, Standard Deviation, Variance
Hypothesis Testing for differences
- t-tests ( 1-sample test , 2-smaple test and paired t-test)
- Analysis of Variance (ANOVA)
Hypothesis Testing for Relations
- Graphing scatter diagram
- Correlations and Regression Analysis (Simple linear regression)
- graphing results
Decision Analysis using the results
Interpretation, sensitivity analysis, writing report, conclusion and more.

- Review of Descriptive Statistics
Scale of Measurement
- Tests & Assumptions
- Multivariate Statistics
Why Study Multivariate Statistics
Univariate and Bivariate Statistics
- Reliability
- Data Appropriate for Multivariate Statistics
Correlation and Partial Correlation
Hypothesis Testing
- Multiple Regression  (1)
General Purpose and Description
Theoretical Issues: Assumptions
Data Analysis and Interpretation
- Multiple Regression (2)
Factor Analysis (Exp. & Com.)
Basics of Logistic Regression (LR)
Interpretation of LR
- Statistical technics to compare groups (1)
Non Parametric statistics
T test
One way ANOVA
- Statistical technics to compare groups (2)
Two ways ANOVA
MANOVA
Analysis of Covariance (ANCOVA)
Introduction to SEM (Structural Equation Modelling)