Professional Diploma in Advanced Statistical Analysis and Structural Equation Modeling (SEM): An Integrated Applied Approach via SPSS, Amos, and Eviews

Program Introduction

Diploma Objectives

Level 1: Foundations of Statistics and Data Preparation

• Introduction to statistics, variable types, data categories, collection methods, and survey design.
• Understanding sampling techniques and determining optimal sample size based on research methodology.
• Data coding, preparation, and cleaning protocols using SPSS.
• Scientific foundations for formulating and deriving hypotheses, statistical testing procedures, and the logic of statistical decision-making.
• Types of statistical decision errors (Type I and Type II) and strategies for mitigation.
• Descriptive statistics and qualitative data summaries.
• Basic statistical analysis techniques, result extraction, and interpretation.
• Outlier detection, treatment, and their impact on significance and statistical errors.
• Missing data imputation techniques.
• Significance testing for qualitative and categorical data.
• Measuring effect size for related samples.

Level 2: Quantitative Analysis and Variance Testing

• Difference testing for quantitative data (One-Sample T-test, Paired-Samples T-test, and Independent-Samples T-test).
• Measuring effect size for independent samples.
• Comprehensive Analysis of Variance (One-way ANOVA, Two-way ANOVA, MANOVA, and Repeated Measures MANOVA).
• Calculating Odds Ratio and Relative Risk.
• Multivariate Analysis of Variance (MANOVA) in depth.
• Analysis of Covariance (ANCOVA).
• Data visualization and error detection in datasets and graphics using SPSS and Excel.

Level 3: Instrument Validation and Correlation Analysis

• Scientific principles of questionnaire design, coding methods, and relative importance analysis of items.
• Scale construction and validation: Internal consistency, Reliability (Cronbach’s Alpha, Split-half, Spearman-Brown, and Guttman).
• Discriminant validity and extremes comparison methods.
• Estimating equivalence and homogeneity between groups (Control variables for experimental and quasi-experimental designs).
• Categorization and grouping techniques.
• Statistical methods for converting raw scores to Standardized Scores (Z-scores and T-scores) for aggregating variables using SPSS and Excel.
• Data entry, reliability testing, and validity assessment.
• Correlation Analysis: Quantitative, Ordinal, Nominal, mixed variables, Partial, and Multiple correlation.
• Multiple Linear Regression.

Level 4: Advanced Non-Linear Regression and Predictive Modeling

• Non-linear Regression: Concepts, assumptions, and types (Curve Estimation, Partial Least Squares (PLS), Binary Logistic, Multinomial Logistic, Ordinal, Probit, and Weight).
• Incorporating Binary and Dummy variables in regression models.
• Receiver Operating Characteristic (ROC) Curve analysis.
• Time Series analysis and stationarity testing via Excel and SPSS.
• Repeated and multiple response analysis.
• Decision Trees analysis.
• Neural Networks.

Level 5: Factorial Analysis and Structural Fundamentals

• Exploratory Factor Analysis (EFA).
• Confirmatory Factor Analysis (CFA) using Amos.
• Path Analysis using Amos.
• Discriminant Analysis.
• Cluster Analysis.
• Evaluating results, detecting errors, and implementing remediation strategies.
• Responding to international journal reviewer comments.
• Guidelines on becoming a statistical reviewer for international journals.
• Overview of alternative statistical software packages (SAS, COSTAT, STATISTICA).

Level 6: Structural Equation Modeling (SEM) – Advanced Amos Applications

• Advanced training on Amos software and its core concepts.
• Taxonomy of structural models.
• Components and parameters of SEM models.
• Systematic steps for model construction.
• Factorial Analysis (Exploratory vs. Confirmatory).
• Confirmatory Factor Analysis (CFA) procedures.
• Goodness-of-Fit (GoF) testing and strategies for model refinement.
• Advanced Discriminant Analysis.
• Advanced Cluster Analysis.
• Advanced ROC Curve analysis.

Level 7: SEM – Advanced Path, Mediation, and Bayesian Estimation

• Advanced Path Analysis.
• Mediation Analysis: Full vs. Partial Mediation.
• Measuring Direct and Indirect effects in single and multiple mediator models.
• Isolating mediator effects: (Baron-Kenny, Sobel z-test, Bootstrapping, and Advanced Modeling).
• Integrating Modeling with Bootstrapping techniques.
• Moderated Mediation and Mediated Moderation (Conditional Indirect Effects).
• Distinguishing between Mediator and Moderator variables.
• Dual-Stage Moderated Mediation.
• Formulating research questions and hypotheses for complex effect models.
• Multiple-Group Analysis.
• Core components of Structural Equation Modeling (SEM).
• Bayesian Estimation in SEM.

Level 8: Time Series Analysis and Forecasting (Eviews + SPSS)

• Time Series: Definitions, types, and fundamental components.
• Simple Linear Regression for time series.
• Verifying regression assumptions (Pre-tests, theoretical, mathematical, and methodological rigor).
• Multiple Linear Regression forms and assumption verification.
• Econometric issues in multiple regression: non-normality of residuals, Heteroscedasticity, Autocorrelation, Non-linearity, and Multicollinearity.
• Stationarity testing (Unit Root tests and Graphical analysis).
• Remedying non-stationarity through transformations (Logarithms, First Difference, and Second Difference).
• Cointegration Testing (Dickey-Fuller, ADF, Philips-Perron, and KPSS).
• Economic Forecasting: Macro, Micro, Internal, and External methodologies.
• Systematic steps for forecasting using diverse analytical methods.

Target Audience

Program Schedule & Logistics:

Global Session Timings: