CROSS SECTION AND EXPERIMENTAL DATA ANALYSIS USING EVIEWS -- Contents -- Preface -- 1 Misinterpretation of Selected Theoretical Concepts of Statistics -- 1.1 Introduction -- 1.2 What is a Population? -- 1.3 A Sample and Sample Space -- 1.3.1 What is a Sample? -- 1.3.2 What is the Sample Space? -- 1.3.3 What is a Representative Sample? -- 1.3.4 Relationship between the Sample Space, Population, and a Sample -- 1.4 Distribution of a Random Sample Space -- 1.5 What is a Random Variable? -- 1.6 Theoretical Concept of a Random Sample -- 1.6.1 What is a Random Sample in Statistics? -- 1.6.2 Central Limit Theorem -- 1.6.3 Unbiased Statistics based on Random Samples -- 1.6.4 Special Notes on Nonrandom Sample -- 1.7 Does a Representative Sample Really Exist? -- 1.8 Remarks on Statistical Powers and Sample Sizes -- 1.9 Hypothesis and Hypothesis Testing -- 1.10 Groups of Research Variables -- 1.10.1 Problem Indicators -- 1.10.2 Controllable Cause Factors -- 1.10.3 Uncontrollable Cause Factors -- 1.10.4 Background or Classification Factors -- 1.10.5 Environmental Factors -- 1.11 Causal Relationship between Variables -- 1.11.1 Bivariate Correlation -- 1.11.2 Special Remarks -- 1.12 Misinterpretation of Selected Statistics -- 1.12.1 Standard Error -- 1.12.2 Significance Level and Power of a Test -- 1.12.3 Reliability of a Test or Instrument -- 1.12.4 Validity of a Test or Instrument -- 1.12.5 Reliability and Validity of Forecasting -- 1.12.6 Reliability and Validity of a Predicted Risk -- 2 Simple Statistical Analysis but Good for Strategic Decision Making -- 2.1 Introduction -- 2.2 A Single Input for Decision Making -- 2.2.1 A Single Sampled Unit -- 2.2.2 Descriptive Statistics Based on a Single Measurable Variable -- 2.2.3 Agung Six-Point Scale (ASPS) Problem Indicator -- 2.2.4 Latent Variables and Composite Indexes.

2.2.5 Demographic and Social-Economic Factors -- 2.2.6 Garbage as a Data Source -- 2.2.7 Boxplot as an Input for Decision Making -- 2.2.8 A Series of Inputs for Strategic Decision Making -- 2.3 Data Transformation -- 2.3.1 To Generate Categorical Variables -- 2.3.2 To Generate Dummy Variables -- 2.4 Biserial Correlation Analysis -- 2.5 One-Way Tabulation of a Variable -- 2.6 Two-Way Tabulations -- 2.6.1 Measure of Associations for Bivariate Categorical Variables -- 2.6.2 Other Measures of Association Based on a 2 X 2 Table -- 2.6.3 Measures of Association Based on a I X 2 Table -- 2.7 Three-Way Tabulation -- 2.7.1 Conditional Measures of Association for a 2 X 2 X 2 Table -- 2.7.2 Conditional Odds Ratio for an I X J X 2 Table -- 2.8 Special Notes and Comments -- 2.9 Special Cases of the N-Way Incomplete Tables -- 2.10 Partial Associations -- 2.11 Multiple Causal Associations Based on Categorical Variables -- 2.11.1 Theoretical and Empirical Concepts of Causal Associations -- 2.11.2 Multidimensional Frequency Table -- 2.12 Seemingly Causal Model Based on Categorical Variables -- 2.12.1 Causal Association Based on (X1, X2, Y1) or (X1, Y1, Y2) -- 2.12.2 Causal Association Based on (X1, X2, Y1, Y2) -- 2.12.3 Causal Association Based on Multidimensional Variables -- 2.13 Alternative Descriptive Statistical Summaries -- 2.13.1 Application of the Object "Descriptive Statistics and Test" -- 2.13.2 Application of the Object "Graph. . ." -- 2.14 How to Present Descriptive Statistical Summary? -- 2.14.1 DSS Based on a Set of Zero-One Indicators -- 2.14.2 Two-Dimensional DSS of Proportions -- 2.14.3 Multidimensional DSS of Proportions -- 2.14.4 DSS Based on a Set of Agung-Likert Scale Attributes -- 2.14.5 DSS Based on a Set of Numerical Problem Indicators -- 2.14.6 Additional Descriptive Statistical Summaries -- 2.15 General Seemingly Causal Model.

2.16 Empirical Studies Presenting Descriptive Statistical Summaries -- 2.16.1 Studies in the Field of Nutrition -- 2.16.2 Studies in Public Health -- 2.16.3 Selected Experimental Studies -- 2.16.4 Studies in Public Relations -- 2.16.5 Studies on Other Population Problems -- 3 One-Way Proportion Models -- 3.1 Introduction -- 3.2 One-Way Proportion Models Based on a 2 X 2 Table -- 3.2.1 Regression Functions -- 3.2.2 Binary Logit Functions -- 3.2.3 Odds Ratio Statistics -- 3.3 Binary Choice Models Based on a K X 2 Table -- 3.3.1 Binary Logit Models -- 3.3.2 Binary Multiple Regressions -- 3.4 Binary Logit Models Based on N-Way Tabulation -- 3.4.1 Binary Logit Models Based on Three-Way Tabulation -- 3.4.2 Binary Choice Models Based on Higher Dimensional Tables -- 3.5 General Binary Choice Models -- 3.5.1 Binary Multiple Regression Model -- 3.5.2 The Wald Test -- 3.5.3 Binary Logit Models -- 3.5.4 Binary Probit Models -- 3.5.5 Binary Extreme-Value Models -- 3.6 Special Notes and Comments -- 3.6.1 The True Population Binary Choice Model -- 3.6.2 The Sampled Binary Choice Function -- 3.6.3 Alternative Equation Estimations -- 3.7 Association between Categorical Variables -- 3.7.1 Generating the Dummy Variables -- 3.7.2 Generating a Cell Factor -- 3.8 One-Way Binary Choice Models Based on N-Way Tabulation -- 3.8.1 N-Way Tabulation without an Empty Cell -- 3.8.2 N-Way Tabulation with Empty Cells -- 3.8.3 Testing Hypotheses -- 3.9 Special Notes and Comments on Binary Choice Models -- 4 N-Way Cell-Proportion Models -- 4.1 Introduction -- 4.2 The N-Way Tabulation of Proportions -- 4.2.1 A 2 X 2 Table of Proportions -- 4.2.2 A I X J Table of Proportions -- 4.3 The 2 X 2 Factorial Model of Proportions -- 4.3.1 Pure Interaction Models -- 4.3.2 Interaction Models with a Main Factor -- 4.3.3 Interaction Models with Both Main Factors.

4.3.4 Additive Binary Choice Models -- 4.4 I X J Factorial Models of Proportions -- 4.4.1 Interaction Models -- 4.4.2 Special Notes and Comments -- 4.5 Multifactorial Cell-Proportion Model -- 4.6 Presenting the Statistical Summary -- 5 N-Way Cell-Mean Models -- 5.1 Introduction -- 5.2 One-Way Multivariate Cell-Mean Models -- 5.2.1 An MCMM without an Intercept -- 5.2.2 An MCMM with Intercepts -- 5.3 N-Way Multivariate Cell-Mean Models -- 5.3.1 Two-Way Multivariate Cell-Mean Models -- 5.3.2 Three-Way Multivariate Cell-Mean Model -- 5.3.3 N-Way Multivariate Cell-Mean Model -- 5.4 Equality Test by Classification -- 5.5 Testing Weighted Means Differences -- 5.6 Descriptive Statistical Summary -- 6 Multinomial Choice Models with Categorical Exogenous Variables -- 6.1 Introduction -- 6.2 Multinomial Choice Models -- 6.2.1 Multinomial Logit Model as a Set of (M ― 1) Binary Logit Models -- 6.2.2 Multinomial Logit Model as a Set of M Binary Choice Models -- 6.3 Ordered Choice Models -- 6.3.1 Simple Ordered Choice Models -- 6.4 Concordance-Discordance Measure of Association -- 6.5 Multifactorial Ordered Choice Models -- 6.6 Multilevel Choice Models -- 6.6.1 Two-Level Choice Models -- 6.6.2 Three-Level Choice Models -- 6.7 Special Notes on the Multinomial Logit Model -- 6.8 Selected Population Studies Using Multinomial Choice Models -- 6.8.1 Multinomial Problem Indicators and Gender Equity Indexes -- 6.8.2 Multinomial Problem and Poverty Indicators -- 7 General Choice Models -- 7.1 Introduction -- 7.2 Binary Choice Models with a Numerical Variable -- 7.2.1 The Simplest Binary Choice Model -- 7.2.2 Alternative Simple Binary Choice Models -- 7.2.3 Special Notes and Comments -- 7.3 Heterogeneous Binary Choice Models -- 7.3.1 The Simplest Heterogeneous Binary Choice Model -- 7.3.2 General Heterogeneous Binary Choice Model -- 7.4 Homogeneous Binary Choice Models.

7.4.1 Binary Choice ANCOVA Model with a Numerical Variable -- 7.4.2 Graphical Representation of an ANCOVA Model -- 7.5 General Binary Choice Models -- 7.5.1 Hierarchical Binary Logit Model -- 7.5.2 Nonhierarchical Binary Logit Model -- 7.5.3 Additive Binary Logit Model -- 7.5.4 GBCM with Two Numerical and a Dichotomous Independent Variable -- 7.5.5 GBCM with Two Numerical and a Set of Categorical Independent Variables -- 7.6 Advanced Binary Choice Models -- 7.6.1 Binary Choice Heterogeneous Regressions -- 7.6.2 Binary Choice ANCOVA Model -- 7.6.3 Descriptive Statistical Summaries -- 7.7 Multidimensional Binary Choice Translog Linear Model -- 7.8 Piecewise Binary Choice Models -- 7.9 Ordered Choice Models with Numerical Independent Variables -- 7.10 Studies Using General Choice Models -- 7.11 Two-Stage Binary Choice Model -- 8 Experimental Data Analysis -- 8.1 Introduction -- 8.2 Analysis Based on Cell-Mean Models -- 8.2.1 The Simplest Statistical Analysis -- 8.2.2 Special Remarks -- 8.2.3 Application of Multivariate Cell-Mean Models -- 8.3 Bivariate Correlation Analysis -- 8.4 Effects of the Experimental Factors -- 8.5 Effects of the Experimental Factors and Covariates -- 8.5.1 Effects of the Experimental Factors and a Covariate -- 8.5.2 Effects of the Experimental Factors and Two Covariates -- 8.5.3 The Application of Translog Linear Models -- 8.6 Application of the Ordered Choice Models -- 8.7 Application of Seemingly Causal Models -- 8.7.1 The Simplest Seemingly Causal Model -- 8.7.2 Four Pairs of Causal Relationships -- 8.7.3 Five Pairs of Causal Relationships -- 8.7.4 All Pairs Have Causal Relationships -- 8.7.5 Alternative Seemingly Causal Models -- 8.7.6 Special Notes and Comments -- 8.8 Multivariate Analysis of Covariance -- 8.9 Tests for Equality of Medians -- 8.10 The Simplest Experimental Design.

9 Seemingly Causal Models Based on Numerical Variables.