Author name: AM

Cox Regression Cox regression, also known as the Cox proportional hazards model, is a statistical technique used to explore the relationship between the survival time of subjects and one or more predictor variables. It is used in medical research, particularly for time-to-event data, where the goal is to investigate how certain factors influence the time […]

Exploratory Factor Analysis Exploratory Factor Analysis (EFA) is a statistical technique to identify underlying relationships between measured variables. It aims to uncover the latent structure (factors) within a set of observed variables without imposing any preconceived structure on the outcome. EFA is often employed in the early stages of research to explore a dataset’s dimensionality

Confirmatory Factor Analysis Confirmatory Factor Analysis (CFA) is a statistical technique used to test the hypothesis that a relationship exists between observed variables and their underlying latent constructs. Unlike Exploratory Factor Analysis (EFA), which explores the data to identify potential underlying structures without preconceived notions, CFA confirms whether the data fit a hypothesized measurement model.

Hierarchical Linear Modeling Hierarchical Linear Modeling (HLM), also known as multilevel modeling or mixed-effects modeling, is a statistical technique used to analyze nested data. This approach is particularly useful when dealing with data where observations are grouped at multiple levels, such as students within classrooms, employees within companies, or patients within hospitals. HLM accounts for

Time series analysis is a statistical technique used to analyze data points collected or recorded at specific intervals. Unlike other data types, time series data are chronological, meaning each data point depends on the preceding values. This characteristic makes time series analysis particularly useful for identifying trends, cycles, and seasonal patterns and forecasting future values

Moderation analysis is a statistical technique used to examine whether the strength or direction of the relationship between an independent variable (X) and a dependent variable (Y) changes across levels of a third variable, known as the moderator (M). It explores the conditions under which specific effects occur, offering insights into the variability of the

Mediation Analysis Mediation analysis is a statistical method used to understand the mechanism through which an independent variable (X) influences a dependent variable (Y) via a third variable, known as the mediator (M). It helps researchers determine whether the presence of M can wholly or partially explain the relationship between X and Y. The classic

Structural Equation Modeling Structural Equation Modeling (SEM) is a statistical technique that allows researchers to examine complex relationships among observed and latent variables. It is a comprehensive method that combines factor analysis and multiple regression analysis, enabling the analysis of both measurement and structural models simultaneously. In simpler terms, SEM allows us to understand the