Boon Yih Mah
Feb 17, 20236 min
Updated: Mar 26
"Not every study is based on a theory or conceptual model, but every study has a framework."
(Polit & Hungler, 1999)
A framework can also assist researchers in determining the best technique and methodologies to use in their research endeavours. In technical terms, a research framework clearly displays the study plan's structure and assists the researcher in developing suitable research questions. It is a sketch or paradigm that depicts the issue to be investigated, the variables and background, and how they connect to or impact one another. The research framework is divided into two types: conceptual and theoretical.
A conceptual framework is a researcher's idea of approaching the research topic. This is evident in the theoretical framework of a much larger resolution scale. It describes your study's significant variables and how they are related. It also describes the entire study's input, procedure, and result. It will educate your readers about the findings of your investigation. The research paradigm is another name for the conceptual framework since it is created before gathering data and is frequently depicted visually. As a result, before you begin collecting data, you should create a conceptual framework by following the research flow and stages below.
An outstanding conceptual framework can be completed by reporting it through thesis writing and oral defence as the last stages if the previous stages yield positive outcomes. Conversely, stages 3 to 8 must be rechecked for accuracy and objectivity. Below are the research flow and respective stages.
Research area conceptualisation
Establish the broad area of study interest.
Review the literature.
Ask fundamental questions.
Grasp what research has previously been done on this topic.
Contribute to it.
Research purpose and objectives identification
Identify the research purpose and objectives.
Problem definition
Define the problem statement.
Research design
Ensure your thesis contributes to and fills a knowledge gap.
Conceptual framework construction
Introduce and clarify the topic.
Decide on all variables and their relationships.
Construct the research flow.
Represent the research flow with the variables in a diagram.
Literature review
Conduct a comprehensive or systematic literature review.
Analyse the link between the particular factors described in the literature.
Theoretical framework formulation
Identify variables.
Characterise and explain relationships.
Propose probable relationships between rising variables.
Formulate a theoretical framework.
Map out or depict the theoretical strands in some diagrammatic format.
Hypothesis formation
Form the hypotheses.
Data collection, analyses, and interpretation
Conduct deductively by referring back to the objectives, research questions or hypotheses.
Are research objectives achieved?
Are research questions answered? or
Are hypotheses substantiated?
Yes (Continue to the next stage.)
No (Reconsider stages 3 to 8)
Analyse results in refining and consolidating new and uncertain variables into observable constructs relevant to the research topic.
Thesis writing
Oral defence
Understanding what a variable means and the various types of variables is critical since a framework provides the conceptual foundation for further research. A sound framework identifies the significant variables in the context that are relevant to the problem statement. It objectively describes the relationships between these variables. The links between the independent, dependent, moderating, and mediating variables are highlighted if appropriate.
The elaboration of a framework's variables covers why or how we anticipate certain interactions to exist and the type and direction of the correlations among the variables of interest. A schematic illustration of the conceptual model given in the framework will also assist the reader in visualising the linkages of ideas. Any framework should include the following five fundamental elements.
Variables should be identified, explicitly stated, and defined.
A discussion should explain how the relationships among the variables exist and are connected.
This should be done for the major relationships that are hypothesised to exist among the variables.
The type and directional relationships among the variables can be hypothesised based on past study findings.
In that case, there should be an indicator in the discussion as to whether the relationships would be positive or negative.
There should be a clear explanation for why these associations are expected to occur.
Previous study findings might be used to support the claims.
The framework should be depicted schematically so the reader can see and understand the postulated relationships.
Variables are any elements that can have different or varying values. Variables are classified into two types: quantitative and qualitative. Exact (discrete) or changing (continuous) values can be assigned to quantitative variables. Exam results, people's weight, and rope length are all examples of quantitative variables. Gender, ethnicity, and marital status are examples of qualitative variables. Variables are further classified into dependent, independent, moderating, and mediating variables.
The dependent variable is the variable of primary interest to the researcher. The researcher's goal is to understand and describe the dependent variable, explain its variability, or predict it. In other words, the main variable is a viable factor for the investigation.
Through the analysis of the dependent variable (that is, finding what variables influence it), it is possible to find answers to the problem. For example, the manager of a company is interested to know the level of job satisfaction of employees in his company. Because satisfaction among employees can vary from very dissatisfied to very satisfied. thus job satisfaction is the main factor or interest to the manager; thus, it is the dependent variable.
An independent variable is one that influences the dependent variable in either a positive or a negative way. That is, when the independent variable is present, the dependent variable is also present. With a variable unit of increase in the independent variable, there is an increase in the dependent variable also. In other words, the variance depends on the independent variable that accounts for the dependent variable. The relationship between the independent (X) and dependent (Y) variables is illustrated in a schematic diagram as follows:
Why or how does X influence Y? This elucidates why or how an independent variable influences a dependent variable. The total effect is the relationship between X and Y, which is the bivariate regression or Pearson correlation between X and Y. For example, management may assume that a pleasant attitude toward work and adequate training can boost workers' productivity. The dependent variable in this scenario is production level, whereas the independent variables are attitude and training. This is because a pleasant attitude toward work and adequate training may undoubtedly impact workers' productivity levels.
A moderating variable is one that has a considerable conditional impact on the connection between the independent variable and the dependent variable. In other words, including a third variable (the moderating variable) alters the initial connection between the independent and dependent variables. The relationship between the dependent (Y), independent (X) and moderating (Z) variables is illustrated in a schematic diagram as follows:
The moderator, Z, alters the connection between X and Y. It impacts the intensity and direction of X and Y's connection. Thus, the influence of X on Y might vary depending on Z. The moderator effect is represented by the interaction term, which may be calculated by multiplying the independent variable by the moderator (X*Z). Use hierarchical multiple regression analysis to enter the two independent variables (X and Z) in Step 1, the interaction term in Step 2, and Y as the dependent variable.
A mediator mediates the relationship between the independent and dependent variables. The indirect effect happens when the independent variable affects a mediator, which causes the mediator to affect the dependent variable. The direct impact is the connection between the independent and dependent variables in the presence of a mediator. The mediator effect happens when there is a statistically significant indirect impact and the direct effect is less than the overall effect.
In the analysis, the bivariate correlations between X and Z and the correlations between Z and Y are highlighted. Hence, multiple regression is utilised to determine the direct and indirect effects, with X and Z as independent variables and Y as the dependent variable. An explanation of why the variables act as mediators should be provided.
A mediator is a plausible explanation, while a moderator is an influencer to the extent of the effect of X on Y. In theory, the mediator is caused by the independent variable; conversely, no directional link is assumed between the independent variable and a moderator. Mediation analyses are used to explain relationships, while moderation analyses are used to understand what variables affect the strength and direction of a relationship. If there are any moderating variables, it explains how and what particular connections they would moderate.
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