SEVENTH EDITION (2026)
INTRODUCTION
Research Philosophy, Ontology, Epistemology
Theory, Constructs, Propositions, Logic, Attributes of a Good Theory, Theory Building
Qualitative Research: Case Study, Phenomenology, Field Research, Ethnographic Research, Grounded Theory
Probabilistic & Nonprobabilistic Sampling
Consistency, Reliability, Validity, Biases
True/Quasi-Experimental Design
THE BASICS
Central Tendency, Spread, Skew, Kurtosis
Probability, Bayes’ Theorem, Trees, Combination, Permutation
PDF, CDF, ICDF, Binomial, Hypergeometric, Poisson, Bernoulli, Discrete Uniform, Geometric, Negative Binomial, Pascal,
Arcsine, Beta, Cauchy Lorentzian, Breit Wigner, Chi-Square, Cosine, Double Log, Erlang, Exponential, Extreme Value
Gumbel, F Fisher Snedecor, Gamma Erlang, Laplace, Logistic, Lognormal, Normal, Parabolic, Pareto, Pearson V,
Pearson VI, PERT, Power, Student’s T, Triangular, Uniform, Weibull/Rayleigh
Classical, Standard, P-Value, CI
Central Limit Theorem
Type I-IV Errors, Sampling Biases
Data Types & Collection Design
ANALYTICAL METHODS
T-Tests: Equal/Unequal/Paired Variance, F-Test, Z-Test
ANOVA, Blocked, Two-Way, ANCOVA, MANOVA
Linear/Nonlinear Correlation
Normality & Distributional Fitting: Kolmogorov-Smirnov, Chi-Square, Akaike Information Criterion, Anderson-Darling, Kuiper’s, Schwarz/Bayes, Box-Cox
Nonparametrics: Runs, Wilcoxon, Mann-Whitney, Lilliefors, Q-Q, D’Agostino-Pearson, Shapiro-Wilk-Royston, Kruskal-Wallis, Mood’s, Cochran’s Q, Friedman’s
Inter/Intra-Rater Reliability, Consistency, Diversity, Internal/External Validity, Predictability
Cohen’s Kappa, Cronbach’s Alpha, Guttman’s Lambda, Inter-Class Correlation, Kendall’s W, Shannon-Brillouin-Simpson Diversity, Homogeneity, Grubbs Outlier, Mahalanobis, Linear & Quadratic Discriminant, Hannan-Quinn, Diebold-Mariano, Pesaran-Timmermann, Precision, Error Control
Linear/Nonlinear Multivariate Regression
Multicollinearity, Heteroskedasticity
Structural Equation Modeling (SEM), Partial Least Squares (PLS)
Endogeneity, Simultaneous Equations Methods, Two-Stage Least Squares
Granger Causality, Engle-Granger
Advanced Regressions: Bootstrap, Cox, Deming, DiD, Logit/Probit/Tobit, Poisson, RDD, Ridge, VAR, Weighted
ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING (DATA SCIENCE)
Bagging Bootstrap
Classification and Regression Trees CART
Custom Fit
Dimension Reduction Principal Component Analysis
Dimension Reduction Factor Analysis
Ensemble Common Fit
Ensemble Complex Fit
Ensemble Time-Series
Gaussian Mix & K-Means Segmentation
K-Nearest Neighbors
Linear Fit Model
Multivariate Discriminant Analysis (Linear)
Multivariate Discriminant Analysis (Quadratic)
Neural Network (Cosine, Tangent, Hyperbolic)
Logistic Binary Classification
Normit-Probit Binary Classification
Phylogenetic Trees & Hierarchical Clustering
Random Forest
Segmentation Clustering
Support Vector Machines SVM
FORECASTING AND PREDICTIVE MODELING
Forecasting Techniques
Time-Series Analysis
Stepwise Regression
Stochastic Forecasting
Nonlinear Extrapolation
Box-Jenkins ARIMA
J-Curve, S-Curve
GARCH
Markov Chain
GLM/MLE: Logit, Probit, Tobit
Cubic Spline, Neural Network, Combinatorial Fuzzy Logic
Trendlines, RMSE, MSE, MAD, MAPE, Theil’s U
Outliers, Nonlinearity, Multicollinearity, Heteroskedasticity, Autocorrelation, Structural Breaks
Functional Forms
Forecast Intervals, OLS, Fix Autocorrelation
MONTE CARLO SIMULATION
Confidence Intervals, Correlations, Precision, Tornado, Sensitivity, Fitting, Percentile Fit, Bootstrapping, Distributional Analysis, Scenarios, Structural Break, Detrending, Deseasonalizing
OPTIMIZATION
Algorithms: Continuous & Discrete Optimization
Efficient Frontier & Stochastic Optimization
