Scales:

• Nominal Ordinal Interval Ratio

• What Techniques Apply to which Type of Data

Data Manipulation:

• Data Transformation

• Output Management

• Re-Coding

• If..Else Stmt

Visualisation in SPSS:

• Basics of charting: Legacy Graphs in SPSS

• Modern Charting Techniques in SPSS

• How to Customise Charts to Better Represent different types of Data

Descriptive analysis of data:

• Frequencies

• Descriptives

• Explore

• Crosstabs

• Charts

What is Significance:

• Statistical Tests for significance: Chi Square, t test, z test F test

• Test of Normality

Statistical Tests:

• Means

• T-test

• One-way ANOVA

• Non parametric tests

• Normality tests

Correlation

• How To Read a Correlation Table

• Data Entry for Correlational Analysis

• Choice of a Suitable Correlation Coefficient

• Non-Parametric Correlation (Kendall’s Tau)

• Parametric Correlation (Pearson’s, Spearman’s)

• Special Correlation (Biserial, Point-Biserial)

• Partial and Distance Correlation

Regression:

• Assumptions behind Linear Regression.

• Running a Basic Linear Regression Model

• Checking for Model Fit

• Model Diagnostics

• What Steps to Take to Improve Model Fit

• Multiple Regression (Linear)

• The method of Least Squares

• Linear Modelling

• Assessing the Goodness of Fit

• Simple Regression

• Multiple Regression (sum of squares, R and R2 , hierarchical, step-wise)

• Choosing a Method Based on Your Research Objectives

• Checking the Accuracy of Regression Model

• Logistic Regression

• Reporting the Output in APA Format

Basics of Sampling: Simple Random, Stratified Sampling

Tables:

• Basic Frequencies

• Descriptive Stats

• Custom Tables for Reports and Slides.

Multivariate Analysis [Factor Analysis]:

• Principal Component Analysis (PCA)

• Role of PCA in Data Analysis

• How to Choose Data for PCA

• Examining Options for Extracting Principal Components

• Interpreting PCA Output

• How are PCA Results used in Regression Modelling

• Theoretical Foundations of Factor Analysis

• Exploratory and Confirmatory Factor Analysis

• Testing Data Sufficiency for EFA & CFA

• Principal Component Analysis

• Factor Rotation

• Factor Extraction

• Factor Analysis for Test Construction

• Interpreting the SPSS output: KMO & Bartlett’s Test

• Correlation Matrix

• Anti-Image

• Explaining the Total Variance

• Communalities

• Eigen-Values

• Scree Plot

• Rotated Component Matrix

• Component Transformation Matrix

• Factor Naming

Multivariate Analysis [Cluster Analysis]:

• Basic Concepts

• K-Means How it Works

• How to Prepare Data for Cluster Analysis

• No of Clusters: How to Make an Informed Choice

• Options for Interobject Distances and Clustering Method

• Formatting and Visualisation of Cluster Results for Presentation

• Interpreting Cluster Analysis Output

• Cluster Analysis Purpose and Uses

• Selecting Distance Measures

• K-Means Clustering and Hierarchical Clustering

• Combining the Clusters

• Working with the SPSS Output: Agglomeration Schedule

• Proximity Matrix

• Cluster Membership

• Icicle Plot and Dendograms

• Transform Values and Transform Measures