Exploring the methodological framework and importance of statistical analysis in quantitative management research
Year: 2025
Type: article
Abstract: Purpose: This paper aims to examine the key stages and methodological considerations of quantitative research in management sciences. It focuses on how theoretical constructs are translated into measurable variables and investigates the effectiveness and challenges of operationalisation, hypothesis testing, and statistical interpretation. Design/methodology/approach: The study adopts a structured, methodological approach to quantitative research, emphasizing statistical techniques such as correlation and regression analysis. It begins with a literature review, followed by hypothesis formulation, data collection, and statistical testing. The scope encompasses both theoretical underpinnings and empirical practices commonly applied in management research. Findings: The research reveals that while quantitative methods enable the construction and validation of complex models, they also encounter challenges related to multidimensional constructs, sampling limitations, and the risk of statistical errors. Descriptive statistics provide a necessary foundation, but it is the application of inferential methods and careful hypothesis testing that ultimately determine the reliability of research conclusions. The analysis highlights the critical role of aligning research objectives with statistical techniques and managing risks associated with Type I and Type II errors. Research limitations/implications: Limitations include potential sampling bias, issues of representativeness, and the constraints of data collection methods. Future research is encouraged to explore more diverse sampling strategies and integrate longitudinal studies to capture dynamic changes over time. Practical implications: The study provides guidance for researchers and practitioners on enhancing the accuracy and validity of quantitative analysis. It offers methodological insights that can improve the design of data-driven decision-making processes in management and policy formulation. Social implications: Improved methodological rigor in management research contributes to more effective policy-making and organizational practices, thereby supporting evidence-based strategies with broader societal impact. Originality/value: This paper contributes to the literature by offering a critical, structured examination of the quantitative research process in management science. It provides value to early-career researchers, academics, and practitioners seeking to improve the quality and applicability of empirical research. Keywords: Quantitative research, hypothesis testing, operationalisation, statistical analysis, management science. Category of the paper: Research paper.
Authors: Henryk Dźwigoł, Mariola Dźwigoł-Barosz
Institution: Silesian University of Technology
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
OpenAlex ID: W4411729729