Synthesis of Quantitative and Qualitative Research Metodology and Techniques in Scientific Studies

In the field of scientific research, a comprehensive understanding of methodology and data collection and analysis techniques serves as the fundamental basis that determines the quality of research outcomes. Quantitative and qualitative research have distinct characteristics, yet both complement each other in revealing scientific reality. The synthesis of these two approaches produces a comprehensive framework that not only measures phenomena objectively but also seeks to understand their deeper meaning. Therefore, understanding data, variables, population, sampling, data collection techniques, and analytical methods is an essential step toward producing valid and meaningful research.

First, data in research can be categorized into two main types: quantitative and qualitative. Quantitative data are numerical in nature and can be statistically analyzed to test hypotheses, while qualitative data are presented in the form of narratives or descriptions aimed at understanding context, meaning, and human experiences related to a phenomenon. Variables are key elements in research, representing measurable characteristics that vary among objects. There are independent variables (X) as causes, dependent variables (Y) as effects, moderating variables (W) that influence the strength of relationships between variables, and mediating variables (M) that act as intermediaries in causal processes. To measure these variables, researchers use different scales of measurement: nominal, ordinal, interval, and ratio. The nominal scale classifies categories without order, the ordinal scale indicates rank or level, the interval scale shows equal distances without an absolute zero, while the ratio scale has equal intervals and an absolute zero as the starting point. To ensure trustworthy research results, validity and reliability testing are required. Validity ensures that an instrument accurately measures the intended concept, while reliability guarantees that the results remain consistent when measurements are repeated.

Second, in research, the population refers to the entire group of objects under study, whereas the sample represents a portion of the population selected to stand in for the whole. Sampling techniques can be carried out through two main approaches: probability and non-probability sampling. In probability sampling, every member of the population has an equal chance of being selected, as seen in random sampling, stratified sampling, or cluster sampling. Meanwhile, non-probability sampling is more subjective, based on specific considerations, such as purposive, quota, or snowball sampling. Instruments used for data collection include questionnaires to measure opinions or attitudes, observations to record direct behaviors, interviews to obtain in-depth information, tests to assess abilities or achievements, and documentation to analyze data from archives or official records. Before use, these instruments must undergo pilot testing and content validation through expert judgment to ensure their relevance, representativeness, and alignment with research objectives.

Third, data collection techniques differ between quantitative and qualitative research. Quantitative research relies on questionnaires, tests, and documentation that produce numerical data suitable for statistical analysis. In contrast, qualitative research employs in-depth interviews, participatory observations, and document analysis to understand the meaning and context of social phenomena. To ensure the accuracy and consistency of data, researchers must apply data triangulation, pilot testing of instruments, enumerator training, and systematic documentation. Moreover, ethical standards must be upheld throughout the data collection process, including obtaining informed consent from participants, maintaining confidentiality, ensuring transparency about the purpose of the study, and avoiding any form of manipulation or coercion toward respondents.

Fifth, data analysis in research serves to reveal the relationships and effects among variables. Correlation analysis is used to measure the strength of association between two or more variables, utilizing tests such as Pearson's correlation for interval or ratio data with normal distribution, Spearman's rank correlation for ordinal data, and Kendall's tau for non-parametric data or small samples. Meanwhile, causal analysis is performed through regression---simple linear regression for one independent variable and one dependent variable, or multiple linear regression for several independent variables influencing a single dependent variable. The interpretation of analytical results involves testing hypotheses, comparing findings with previous studies, and discussing implications as well as the limitations of the research.

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*) This paper is a reference from Part 6: Methodology and Techniques of Quantitative and Qualitative Research, derived from the course material of Research Methods in Management, instructed by Prof. Dr. H. A. Rusdiana, M.M.

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