USE OF REGRESSION IN SOCIAL SCIENCE RESEARCH: BEYOND LINEAR MODELS
DOI:
https://doi.org/10.33003/jaat.2026.1202.06Keywords:
Data,, Regression,, Research,, Social research,, ToolAbstract
Research aims to systematically acquire knowledge through data collection, analysis, and interpretation to answer questions and enhance understanding. Therefore, this study seeks to provide insight into the use of regression in social science research, extending beyond linear models. Social research in agricultural extension aims to understand the dynamics, behaviours, and felt needs of rural communities to improve resource distribution and innovation, ultimately increasing agricultural productivity and informing policy decisions. A robust methodology was employed to establish a clear research framework, sampling methods, and data analysis techniques, ensuring the reliability and validity of the results. Social science research outcomes established relationships between dependent and independent variables, focusing on estimating or predicting outcomes as well as managing the effects of independent variables. Regression analysiss is useful and powerful for interpreting, validating, reporting, and explaining covariates. This study highlights the uniqueness of regression compared to other test tools, and the study outlines the basic criteria for selecting appropriate regression models. In conclusion, it underscores the assumptions behind both linear and nonlinear regression and emphasizes the advantages of regression analysis over other testing tools in social science research. Additionally, the study provides insight into various types of nonlinear regression, including their strengths and limitations. Therefore the study recommends that researchers should strive to thoroughly understand test tools, seek further understanding, and employ both linear and nonlinear regression techniques for accurate data interpretation.
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