Research methodology basics

 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 ...

 The Residual Sum of Squares (RSS) , also known as the Sum of Squared Errors (SSE) , is a measure of the discrepancy between the actual data points and the values predicted by a regression model. 📌 Definition: RSS = ∑ ( Y i − Y ^ i ) 2 \text{RSS} = \sum (Y_i - \hat{Y}_i)^2 Where: Y i Y_i ​ = actual value of the dependent variable Y ^ i \hat{Y}_i ​ = predicted value from the regression model Y i − Y ^ i Y_i - \hat{Y}_i ​ = residual or error term 🎯 What Does RSS Represent? RSS quantifies the total amount of variation in the dependent variable that is not explained by the regression model . A smaller RSS means the model's predictions are closer to actual values → better fit. A larger RSS indicates poor model fit , with more prediction errors. 📊 Where It Fits in Total Variance In regression or ANOVA: Total Sum of Squares (SST) = Explained (SSR) + Residual (RSS or SSE) \text{Total Sum of Squares (SST)} = \...

 The F-Ratio (also called the F-statistic ) is a key concept in ANOVA and regression analysis , used to test whether a model or group of variables significantly explains variation in the dependent variable. 🎯 What is the F-Ratio? The F-Ratio is the ratio of systematic variance (explained by the model or treatment) to unsystematic variance (error or residual variance). In simpler terms: It tells us whether the variation explained by the independent variables is significantly greater than the unexplained (random) variation. 🧮 F-Ratio Formula In ANOVA or regression: F = Mean Square Between (MSB) Mean Square Within (MSW) or F = MSR MSE F = \frac{\text{Mean Square Between (MSB)}}{\text{Mean Square Within (MSW)}} \quad \text{or} \quad F = \frac{\text{MSR}}{\text{MSE}} ​ Where: M S R =   Mean Square due to Regression (Systematic) M S E = S S E n − k − 1 MSE = \frac{SSE}{n - k - 1} ​ → Mean Square Error (Unexplained) 📌 W...