Research methodology basics

 Here are 50 interview questions with answers on Non-Parametric Tests in Research Methodology , ideal for academic, teaching, and research-based interviews.   Basic Conceptual Questions 1.       What is a non-parametric test? A non-parametric test is a statistical test that does not assume a specific distribution of the data, often used when assumptions of parametric tests are not met. 2.       When should you use a non-parametric test? When the data is ordinal, not normally distributed, or when sample sizes are small or variances are unequal. 3.       What is the major difference between parametric and non-parametric tests? Parametric tests assume a known distribution (e.g., normal), while non-parametric tests do not. 4.       Name some common non-parametric tests. Mann–Whitney U test, Wilcoxon signed-rank test, Kruskal–Wallis test, Friedman test, Chi-square tes...

 Here are 50 interview questions with answers on Parametric Tests in Research Methodology , suitable for academic and research interviews: 🔍 Basic Conceptual Questions 1.       What is a parametric test? A parametric test is a statistical test that makes assumptions about the parameters (mean, variance) of the population distribution from which the sample is drawn. 2.       What are the key assumptions of parametric tests? o     The data is normally distributed o     Homogeneity of variances o     Independence of observations o     Interval or ratio level of measurement 3.       Name some common parametric tests. o     t-test (independent, paired) o     ANOVA (One-way, Two-way) o     Pearson’s correlation o     Linear regression 4.      ...

 Your sentence accurately summarizes the advantages of Partial Least Squares Structural Equation Modeling (PLS-SEM) . Here's a clear, expanded explanation of each part of the statement: ✅ “PLS-SEM is a variance-based modeling approach…” Variance-based means PLS-SEM aims to maximize the explained variance (R²) in the dependent (endogenous) variables. Unlike Covariance-Based SEM (CB-SEM) , which focuses on reproducing the entire covariance matrix and overall model fit, PLS-SEM emphasizes prediction and explaining target constructs . ✅ “…gained popularity in the fields of management and social sciences…” PLS-SEM is widely used in: Management research (e.g., leadership, organizational behavior) Marketing (e.g., customer satisfaction, brand loyalty) Information systems (e.g., tech adoption models like TAM/UTAUT) Education and psychology Its popularity stems from its flexibility and suitability for exploratory research and theory building , which ...