Operationalization (MD)

• Operationalization is the process of defining a fuzzy concept and making it clearly distinguishable or measurable
• Operationalization is the process of transforming an abstract concept or theory into an empirical, testable subject of research
• Proper operationalization is crucial in improving the robustness of research design and obtaining relevant results
• Bad operationalization can introduce the researcher's preconceptions or biases into the data or generate responses that do not adequately respond to the research question or test the theory in question
• Linking Conceptualization with Operationalization

  1. Conceptual framework is obtained from the theoretical framework

  2. Operationalization process takes off from conceptual framework

  3. Operational framework is prepared

  4. Research design is developed

  5. Variables and indicators are identified and defined

  6. Data gathering methods and instruments are determined

  7. Sampling plan is developed
• Independent variable


  The cause, its value is independent of the other variables in the study
• Dependent variable


  The effect, its value depends on changes in the independent variable
• Intervening variable


  A hypothetical variable used to explain causal links between other variables, it explains how or why there is a relation between two variables
• Moderator variable


  A variable that affects the strength of the relation between the predictor and criterion variable, it specifies when a relation will hold
• Controlling variable


  Variables or contributing factors that are fixed or eliminated in order to clearly identify the relationship between an independent variable and a dependent variable
• Operational Framework in the Institutionalization of Small and Medium Enterprises

  • Independent variables: leadership style, production policies, market access, human resources, financial resources
  • Dependent variables: technical competence, normative commitment, innovative thrust, environmental image, spread effects of activities, autonomy, influence, public acceptance
• Operational Framework in Analyzing Financial Management as a System of Good Governance

  • Areas: system components, performance measurement, overarching goals
  • Financial management areas: treasury, accounting, budgeting, allied areas
  • Financial performance: liquidity, activity, cost efficiency, profitability, leverage, stability
  • Good governance: transparency, accountability, predictability, participation
• Operational Framework Showing the Phases of the Study

  • Phases: conducting an inventory of organizations or institutions, interpreting the data gathered from the inventory, selection of the units of further studies, analysis of the unit of study
• The review of related literrature is the basis of theoretical framework.
• Theoretical framework refers to the conceptual model used by researchers to guide their research.
• Theories can be defined as statements about relationships among concepts which have been tested through empirical evidence.
• Researchers use theories to explain phenomena they observe in real life situations.
• Hypothesis testing involves collecting data and analyzing it using statistical methods to test whether the hypothesis is supported by the data.
• Hypothesis testing involves collecting data and analyzing it using statistical methods to determine whether there is a significant difference between the expected outcome and the actual result.
• Theory development involves identifying key variables and developing hypotheses based on existing literature.
• Researchers use theories to develop hypotheses, which are testable propositions based on the relationship between two or more constructs.
• A theory is a set of propositions that describe how things are believed to work together in a particular situation.
• Data collection methods include surveys, interviews, observations, experiments, and secondary sources.
• A theory is a set of assumptions that explain why things happen the way they do.
• Research design is the plan for collecting and analyzing data.
• Conceptual model is a visual representation of the relationships among concepts in a theory.
• Data collection techniques include surveys, interviews, focus groups, observation, experiments, and secondary sources such as archival records and existing databases.
• Data analysis techniques include descriptive statistics, inferential statistics, and qualitative analysis.
• Surveys involve distributing questionnaires to collect information on specific topics.
• Operational definitions involve specifying how a concept will be measured or observed in research.
• Inferential statistics are used to make predictions or draw conclusions based on sample data.
• Data collection techniques include surveys, interviews, observations, experiments, and secondary sources such as archival records or published studies.
• Descriptive statistics involve summarizing and presenting numerical data in a meaningful way.
• Interviews are face-to-face conversations between the interviewer and respondent where questions are asked and answers recorded.
• Conceptual definition refers to the meaning or understanding of a term within a particular context or discipline.
• Quantitative research focuses on numerical data and uses statistical analysis to draw conclusions.
• Qualitative research emphasizes non-numerical data and seeks to understand human experiences and perspectives.
• Operational definitions involve defining concepts in terms of observable behaviors or measurable characteristics.
• Operational definition refers to the way a concept will be measured or observed in research.
• Surveys involve asking participants questions through questionnaires or online platforms.