The phenomenon of research methodology provides the essential, logical framework for scientific inquiry, resting on the assumption that a precise understanding and application of its four main pillars are crucial for generating valid findings. The provided text details these pillars---starting with the nature of data and variables, moving through sampling techniques, outlining data collection instruments, and concluding with data analysis. A significant gap exists when researchers fail to strategically align these components---for example, misapplying a non-probability sample when broad generalization is needed, or confusing the scope of correlation with that of regression. Therefore, the primary objective is to solidify the theoretical grasp of these quantitative and qualitative distinctions and guide researchers to congruently apply the correct methodology at every stage, thereby ensuring the structural soundness and empirical rigor of their studies.
First, everything that is studied begins with data, which is a representation of facts, both measurable (quantitative) and observable (qualitative). Quantitative data is in the form of numbers and is used for statistical analysis and hypothesis testing, while qualitative data is in the form of narratives or descriptions that support interpretive analysis and understanding of meaning. Data quality is highly dependent on variables, which are the different characteristics of the research object. Variables are grouped based on their causal role: Independent (X) as the causative or influencing factor, and Dependent (Y) as the result or effect that is influenced. To enrich the model, there are also Moderating Variables (W) that change the strength or direction of the X-Y relationship, and Mediating Variables (M) that explain the mechanism or path of influence of X on Y. Data validity is ensured by Validity (measuring what should be measured) and Reliability (consistency of results), which are prerequisites before the data is processed.
Second, in research, the Population is defined as all objects that are the focus of the study. Due to resource limitations, researchers work with a Sample, which is a representative part of the population that allows for generalization of results. The decision to select this sample is determined by the Sampling Technique. There are two main techniques: Probability Sampling (such as Simple Random or Stratified), where each member of the population has an equal chance of being selected, allowing for broader generalization. Conversely, Non-Probability Sampling (such as Purposive or Quota) uses the researcher's subjective considerations, which is ideal for studies that require specific qualitative data depth. Once the sample is determined, data is collected using Data Collection Instruments, which include Questionnaires (for broad data), Observations (factual data in the field), Interviews (in-depth qualitative data), Tests (objective abilities), and Documents (authentic/historical evidence).
Third, data collection techniques must always be in line with the research approach and objectives. In Quantitative Techniques, the focus is on producing standardized numerical data. The main methods are using Questionnaires with structured scales (such as the Likert scale) and standardized Tests to measure attitudes, opinions, or achievements objectively and efficiently in large samples. Meanwhile, Qualitative Techniques aim to achieve a deep understanding of context and meaning. The dominant techniques include In-depth Interviews to explore the views and reasons of respondents, as well as Participant Observation where researchers are directly involved in recording actual behavior in the field. These two techniques allow for the discovery of insights that are not revealed through numbers.
Fourth, the final stage of the methodology is Data Analysis, which serves to interpret the relationship between variables and test hypotheses. The two main categories of analysis are Correlation and Influence. Correlation Analysis measures the level and direction of the relationship between two variables, as indicated by the r coefficient, which ranges from 1 (strong negative relationship) to +1 (strong positive relationship). The choice of correlation test is tailored to the data scale, for example, Pearson for normally distributed Interval/Ratio data, or Spearman and Kendall for Ordinal data. Meanwhile, Influence Analysis uses Regression to measure the extent to which independent variables influence dependent variables. Simple Linear Regression models test one variable X against one variable Y, while Multiple Linear Regression tests the simultaneous influence of two or more variables X on one variable Y. Regression results, including the coefficient of determination (R2), allow researchers to draw causal and proportional conclusions about the contribution of independent variables.
In research, there are data and variables. A sample is a small part of a population. Techniques for generating data include questionnaires, tests, interviews, observations, and document analysis. Data analysis uses correlation or influence.Â
This text is presented from the Teaching Module of the Research Methodology Course in Islamic Educational Management Part 6 Qualitative and Quantitative Research Methodology and Techniques. Lecturer: Prof. Dr. H. A. Rusdiana., M.M. (https://digilib.uinsgd.ac.id/id/eprint/121673)Â
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